Machine recognition of human handwriting is a very difficult problem, and with the recent explosion of pen-based computing devices, has become an important problem to be addressed. Machine recognition of human handwriting has various present applications.
One example of the current application for machine recognition of human handwriting is found in personal digital assistants. Typically these type of products have a touch sensitive screen upon which a user can impose handwriting. These devices then function to digitize the handwritten input, such as alphanumeric input, and thereafter process the input in an attempt to recognize the information content of the handwriting.
Pursuant to one prior art handwriting recognition technique, one makes a best determination as to the identity of each alphanumeric character in sequence, with the resulting string of characters comprising the result of the recognition activity. There are a variety of drawbacks to this approach. It is hindered by the difficulty of identifying spatial boundaries of the candidate inputs (in this case alphanumeric characters to be recognized. When these boundaries are not located correctly, it is impossible to recognize the character accurately, since it will either be lacking pieces or will incorporate extraneous material from adjacent characters.
One significant problem with machine recognition of human handwriting is the ability to recognize the end of one input and the beginning of the next input. For example, a significant problem exists in locating the end of one handwritten input segment, word, or alphanumeric input, from the beginning of the subsequent handwritten input segment, word, or alphanumeric input. Poor recognition of such breaks in the handwritten input results in poor, inaccurate interpretation of the information content of the handwritten input.
Accordingly, a need exists for a handwriting recognition technique that can detect the end of a first handwritten input segment from the beginning of a second handwritten input segment, in the handwritten input and thereby provide a more accurate interpretation of the information content of the handwritten input.